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add ml Class tool schema
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lidanyang
parent
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commit
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306
metagpt/tools/functions/schemas/data_preprocess.yml
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306
metagpt/tools/functions/schemas/data_preprocess.yml
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FillMissingValue:
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type: class
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description: "Completing missing values with simple strategies"
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "columns to be processed"
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strategy:
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type: str
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description: "the imputation strategy"
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default: mean
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enum:
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- mean
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- median
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- most_frequent
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- constant
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fill_value:
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type: int
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description: "fill_value is used to replace all occurrences of missing_values"
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default: null
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required:
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- features
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fit:
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description: "Fit the FillMissingValue model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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MinMaxScale:
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type: class
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description: "Transform features by scaling each feature to a range, witch is (0, 1)"
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "columns to be processed"
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required:
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- features
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fit:
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description: "Fit the MinMaxScale model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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StandardScale:
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type: class
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description: "Standardize features by removing the mean and scaling to unit variance"
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "columns to be processed"
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required:
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- features
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fit:
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description: "Fit the StandardScale model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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MaxAbsScale:
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type: class
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description: "cale each feature by its maximum absolute value"
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "columns to be processed"
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required:
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- features
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fit:
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description: "Fit the MaxAbsScale model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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LabelEncode:
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type: class
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description: "Apply label encoding to specified categorical columns in-place."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "Categorical columns to be label encoded"
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required:
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- features
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fit:
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description: "Fit the LabelEncode model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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OneHotEncode:
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type: class
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description: "Apply one-hot encoding to specified categorical columns, the original columns will be dropped."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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features:
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type: list
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description: "Categorical columns to be one-hot encoded and dropped"
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required:
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- features
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fit:
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description: "Fit the OneHotEncoding model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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429
metagpt/tools/functions/schemas/feature_engineering.yml
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429
metagpt/tools/functions/schemas/feature_engineering.yml
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@ -0,0 +1,429 @@
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PolynomialExpansion:
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type: class
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description: "Add polynomial and interaction features from selected numeric columns, excluding the bias column."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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cols:
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type: list
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description: "Columns for polynomial expansion."
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degree:
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type: int
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description: "The degree of the polynomial features."
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default: 2
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required:
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- cols
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fit:
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description: "Fit the PolynomialExpansion model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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CatCount:
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type: class
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description: "Add value counts of categorical columns as new features."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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cols:
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type: list
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description: "Columns for value counts."
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required:
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- cols
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fit:
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description: "Fit the CatCount model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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TargetMeanEncoder:
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type: class
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description: "Encodes a categorical column by the mean of the label column, and adds the result as a new feature."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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col:
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type: str
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description: "Column to be mean encoded."
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label:
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type: str
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description: "Predicted label column."
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required:
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- col
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- label
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fit:
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description: "Fit the TargetMeanEncoder model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
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description: "Fit and transform the input DataFrame."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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KFoldTargetMeanEncoder:
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type: class
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description: "Adds a new feature to the DataFrame by k-fold mean encoding of a categorical column using the label column."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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col:
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type: str
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description: "Column to be k-fold mean encoded."
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label:
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type: str
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description: "Predicted label column."
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n_splits:
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type: int
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description: "Number of splits for K-fold."
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default: 5
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random_state:
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type: int
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description: "Random seed."
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default: 2021
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required:
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- col
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- label
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fit:
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description: "Fit the KFoldTargetMeanEncoder model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
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description: "Transform the input DataFrame with the fitted model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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fit_transform:
|
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description: "Fit and transform the input DataFrame."
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parameters:
|
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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CatCross:
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type: class
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description: "Add pairwise crossed features and convert them to numerical features."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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cols:
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type: list
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description: "Columns to be pairwise crossed."
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max_cat_num:
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type: int
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description: "Maximum unique categories per crossed feature."
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default: 100
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required:
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- cols
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fit:
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description: "Fit the CatCross model."
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parameters:
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
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required:
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- df
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transform:
|
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description: "Transform the input DataFrame with the fitted model."
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parameters:
|
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properties:
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df:
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type: DataFrame
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description: "The input DataFrame."
|
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required:
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- df
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returns:
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df:
|
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type: DataFrame
|
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description: "The transformed DataFrame."
|
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fit_transform:
|
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description: "Fit and transform the input DataFrame."
|
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parameters:
|
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properties:
|
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df:
|
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type: DataFrame
|
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description: "The input DataFrame."
|
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required:
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- df
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returns:
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df:
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type: DataFrame
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description: "The transformed DataFrame."
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GroupStat:
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type: class
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description: "Aggregate specified column in a DataFrame grouped by another column, adding new features named '<agg_col>_<agg_func>_by_<group_col>'."
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methods:
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__init__:
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description: "Initialize self."
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parameters:
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properties:
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group_col:
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type: str
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description: "Column used for grouping."
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agg_col:
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type: str
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description: "Column on which aggregation is performed."
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agg_funcs:
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type: list
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description: >-
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List of aggregation functions to apply, such as ['mean', 'std'].
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Each function must be supported by pandas.
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required:
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- group_col
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- agg_col
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- agg_funcs
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||||
fit:
|
||||
description: "Fit the GroupStat model."
|
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parameters:
|
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properties:
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||||
df:
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||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
transform:
|
||||
description: "Transform the input DataFrame with the fitted model."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
fit_transform:
|
||||
description: "Fit and transform the input DataFrame."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
|
||||
SplitBins:
|
||||
type: class
|
||||
description: "Bin continuous data into intervals and return the bin identifier encoded as an integer value"
|
||||
methods:
|
||||
__init__:
|
||||
description: "Initialize self."
|
||||
parameters:
|
||||
properties:
|
||||
cols:
|
||||
type: list
|
||||
description: "Columns to be binned."
|
||||
strategy:
|
||||
type: str
|
||||
description: "Strategy used to define the widths of the bins."
|
||||
default: quantile
|
||||
required:
|
||||
- cols
|
||||
fit:
|
||||
description: "Fit the SplitBins model."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
transform:
|
||||
description: "Transform the input DataFrame with the fitted model."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
fit_transform:
|
||||
description: "Fit and transform the input DataFrame."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
|
||||
GeneralSelection:
|
||||
type: class
|
||||
description: "Drop all nan feats and feats with only one unique value."
|
||||
methods:
|
||||
__init__:
|
||||
description: "Initialize self."
|
||||
parameters:
|
||||
properties:
|
||||
label_col:
|
||||
type: str
|
||||
description: "Label column name."
|
||||
required:
|
||||
- label_col
|
||||
fit:
|
||||
description: "Fit the GeneralSelection model."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
transform:
|
||||
description: "Transform the input DataFrame with the fitted model."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
fit_transform:
|
||||
description: "Fit and transform the input DataFrame."
|
||||
parameters:
|
||||
properties:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The input DataFrame."
|
||||
required:
|
||||
- df
|
||||
returns:
|
||||
df:
|
||||
type: DataFrame
|
||||
description: "The transformed DataFrame."
|
||||
Loading…
Add table
Add a link
Reference in a new issue